diff --git a/content/docs/how-to-guides/download-and-writer-datasets-locally.md b/content/docs/how-to-guides/download-and-writer-datasets-locally.md
new file mode 100644
index 0000000..89632a4
--- /dev/null
+++ b/content/docs/how-to-guides/download-and-writer-datasets-locally.md
@@ -0,0 +1,95 @@
++++
+title = "Download Datasets From Highlighter"
+description = "Download a Dataset from Highlighter and save it to a local file system in a selected format"
+date = 2024-05-20T08:00:00+00:00
+updated = 2024-05-20T08:00:00+00:00
+draft = false
+weight = 100
+sort_by = "weight"
+template = "docs/page.html"
+
+[extra]
+toc = true
+top = false
++++
+
+The Highlighter SDK allows you to download your Datasets from your Highlighter
+account and save it in some common formats.
+
+When converting to common formats sucs as Coco or Yolo things like `entity_id`
+will not be preserved. Only the information nessessary for training will end up 
+in the resulting saved dataset. If you want to save a dataset localled and not 
+loose this information you must use the `hdf` or `json` format.
+
+## CLI
+
+```console
+hl dataset read --help
+Usage: hl dataset read [OPTIONS] COMMAND [ARGS]...
+
+Options:
+  -i, --dataset-ids TEXT  integet <id> or <id>:<split>
+  --page-size INTEGER     [default: 200]
+  --help                  Show this message and exit.
+
+Commands:
+  coco
+  hdf
+  yolo
+```
+
+**Use `--help` to see the format specific cli options** 
+
+For example, the following will:
+  - download dataset 123 and 456
+  - save the images to `/my/image/cache/`
+   - save the annotations as a `coco` dataset to `my_dataset/`
+
+```console
+hl dataset read -i 123:train -i 456:test coco --annotations-dir my_dataset/ --data-file-dir /my/image/cache/
+
+ls my_dataset/
+> test.json train.json
+```
+
+Use `--help` to see the format specific cli Options
+
+```console
+hl dataset read -i 123 yolo --help
+```
+
+# Python API
+
+The following will do same download and conversion as in the CLI example
+
+```python
+from pathlib import Path
+from highlighter.datasets import Dataset 
+from highlighter.datasets.formats.coco import CocoWriter
+from highlighter import HLClient
+
+client = HLClient.from_env()
+
+train_ds = Dataset.read_highlighter_dataset_assessments(
+        client, 123
+        )
+train_ds.data_files_df.loc[:, "split"] = "train"
+
+test_ds = Dataset.read_highlighter_dataset_assessments(
+        client, 456
+        )
+test_ds.data_files_df.loc[:, "split"] = "test"
+
+combined_ds = Dataset.combine([train_ds, test_ds])
+
+annotations_dir = Path("my_dataset/")
+writer = CocoWriter(annotations_dir)
+writer.write(combined_ds)
+
+images_dir = Path("/my/image/cache/")
+Dataset.download_dataset_files(
+        client,
+        images_dir,
+        combined_ds.data_files_df,
+        )
+```